Swarm intelligence is artificial intelligence based on the collective behavior of decentralized, self-organized systems. Swarm intelligence systems are typically made up of a population of simple agents interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local interactions between such agents lead to the emergence of complex global behavior.
Over the last few years, swarm intelligence has become an interesting alternative to standard centralized and distributed control approaches for solving a variety of multi-agent coordination problems. Some implementations include using only local rules, using “digital pheromones,” and using sensor fusion and self-deployment of sensors.
The behavior of local agents employed in a swarm system are typically governed by simple rules, which take the position of neighbors into account. While it is relatively straightforward to obtain the position of neighbors in computer simulations of swarm systems, in a practical, real world situation, positioning information requires particular sensory and functional capabilities, in particular, digital communication. For instance, each agent may require a Global Positioning System (GPS) and digital communication to relay the GPS data to other agents that require the data. In still another example, agents in a swarm system that is based upon “digital pheromones” need to maintain a global map of pheromone positions that is shared among all agents.